HLTA10 Ch 7

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Health Studies
Caroline Barakat

HTLA10 Ch 7 Sample SizeSampling for Quantitative Research Sampling unit a member of the sample popn person organization geog areaEcological fallacy distortions will occur if relsh btw variables are estimatedone level of analysis the clinics and then extrapolated to another the individual patients The converse is known as the individualistic or reductionist fallacy which inferences about groups are drawn f individuals The investigator will need to be clear about the unit of analysis in the proposed study and base the sampling procedures on those units Power calculation the statistical approach to determining sample size in evaluation studies Statistical power a measure of how likely the study is to produce a statistically significant result for a difference btwn groups of a given magnitude the ability to detect a true difference If the statistical power of the study is lowstudy results will be questionable study might have been too small to detect any differences should be greater than 008The probability that a test will produce a significant differencea given level of significance is called the power of the testSubstantive hypothesis the predicted association btwn variablesNull hypothesis a statistical artifice and always predicts the absence of a relsh btwn the variables Hypothesis testing is based in the logic that the substantive hypothesis is tested by assuming that the null hypothesis is true NH involves calculating how likely the probability the results were to have occurred if they really were no differences thus the accountability of proof tests w the SH that there is a change or difference The NH is compared w the research observations and stat tests are used to estimate the probability of the observations occurring by chanceProbability theory the smaller the value of P the more to conclude that the null hypothesis is true and thus be rejected and the substantive hypothesis should be accepted Statistical tests of significance apply probability theory to work out the chances of obtaining the observed resultType I error alpha error the error of rejecting a true null hypothesis that there is no difference the acceptance of differences when none exist false positive Type II error beta error the failure to reject a null hypothesis when it is actually false the acceptance of no differences when they do exist small samples have this problemW a very large sample its almost always possible to reject any null hypothesis type I error as statistics are sensitive to sample size sample which are too small have a risk of a failure to demonstrate a real difference type II error Samples that are too small have a high risk of failing to demo a real difference type II error Sampling size the larger the sample then the smaller will be the sampling error others things being equal and statistically significant results are more likely to be obtained in larger samples Thus w a very large sample its almost always possible to reject any null hypothesis type I error bc stats are sensitive to sample size
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